Econometric Analysis of Count Data

Front Cover
Springer Science & Business Media, Mar 7, 2008 - Business & Economics - 320 pages
0 Reviews
The “count data” ?eld has further ?ourished since the previous edition of this book was published in 2003. The development of new methods has not slowed down by any means, and the application of existing ones in applied work has expanded in many areas of social science research. This, in itself, would be reason enough for updating the material in this book, to ensure that it continues to provide a fair representation of the current state of research. In addition, however, I have seized the opportunity to undertake some major changes to the organization of the book itself. The core material on cross-section models for count data is now presented in four chapters, rather than in two as previously. The ?rst of these four chapters introduces the Poissonregressionmodel,anditsestimationbymaximumlikelihoodorpseudo maximum likelihood. The second focuses on unobserved heterogeneity, the third on endogeneity and non-random sample selection. The fourth chapter provides an extended and uni?ed discussion of zeros in count data models. This topic deserves, in my view, special emphasis, as it relates to aspects of modeling and estimation that are speci?c to counts, as opposed to general exponential regression models for non-negative dependent variables. Count distributions put positive probability mass on single o- comes, and thus o?er a richer set of interesting inferences.
 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Introduction
1
12 Examples
2
13 Organization of the Book
4
Probability Models for Count Data
6
222 Genesis of the Poisson Distribution
10
223 Poisson Process
11
224 Generalizations of the Poisson Process
14
225 Poisson Distribution as a Binomial Limit
15
52 Incidental Censoring and Truncation
148
522 Models of NonRandom Selection
149
523 Bivariate Normal Error Distribution
150
524 Outcome Distribution
152
525 Incidental Censoring
153
526 Incidental Truncation
154
53 Endogeneity in Count Data Models
156
532 Parameter Ancillarity
157

226 Exponential Interarrival Times
16
227 NonPoissonness
17
23 Further Distributions for Count Data
20
232 Binomial Distribution
25
233 Logarithmic Distribution
27
234 Summary
28
24 Modified Count Data Distributions
30
242 Censoring and Grouping
31
243 Altered Distributions
32
25 Generalizations
33
252 Compound Distributions
36
253 Birth Process Generalizations
39
254 Katz Family of Distributions
40
255 Additive LogDifferenced Probability Models
41
256 Linear Exponential Families
42
257 Summary
44
26 Distributions for Over and Underdispersion
45
262 Generalized Poisson Distribution
46
263 Poisson Polynomial Distribution
47
264 Double Poisson Distribution
49
27 Duration Analysis and Count Data
50
271 Distributions for Interarrival Times
52
272 Renewal Processes
54
273 Gamma Count Distribution
56
274 Duration Mixture Models
59
Poisson Regression
63
313 Ordinary Least Squares and Other Alternatives
65
314 Interpretation of Parameters
70
315 Period at Risk
74
32 Maximum Likelihood Estimation
77
323 NewtonRaphson Algorithm
78
324 Properties of the Maximum Likelihood Estimator
80
325 Estimation of the Variance Matrix
82
326 Approximate Distribution of the Poisson Regression Coefficients
83
327 Bias Reduction Techniques
84
33 PseudoMaximum Likelihood
87
331 Linear Exponential Families
89
332 Biased Poisson Maximum Likelihood Inference
90
333 Robust Poisson Regression
91
334 NonParametric Variance Estimation
95
335 Poisson Regression and LogLinear Models
97
336 Generalized Method of Moments
98
34 Sources of Misspecification
102
342 Unobserved Heterogeneity
103
343 Measurement Error
105
344 Dependent Process
107
346 Simultaneity and Endogeneity
108
347 Underreporting
109
349 Variance Function
110
35 Testing for Misspecification
112
352 Regression Based Tests
118
354 Tests for NonNested Models
120
36 Outlook
125
Unobserved Heterogeneity
127
412 Partial Effects with Unobserved Heterogeneity
128
413 Unobserved Heterogeneity in the Poisson Model
129
414 Parametric and SemiParametric Models
130
421 Gamma Mixture
131
423 LogNormal Mixture
132
43 Negative Binomial Models
134
431 Negbin II Model
135
432 Negbin I Model
136
434 NegbinX Model
137
44 Semiparametric Mixture Models
138
442 Finite Mixture Models
139
Sample Selection and Endogeneity
143
511 Truncated Count Data Models
144
513 Censored Count Data Models
146
514 Grouped Poisson Regression Model
147
533 Endogeneity and Mean Function
159
534 A TwoEquation Framework
161
535 Instrumental Variable Estimation
162
536 Estimation in Stages
165
54 Switching Regression
167
541 Full Information Maximum Likelihood Estimation
168
542 MomentBased Estimation
170
543 NonNormality
171
Zeros in Count Data Models
173
62 Zeros in the Poisson Model
174
622 TwoCrossings Theorem
175
623 Effects at the Extensive Margin
176
624 MultiIndex Models
177
63 Hurdle Count Data Models
178
631 Hurdle Poisson Model
181
632 Marginal Effects
182
633 Hurdle Negative Binomial Model
183
635 Unobserved Heterogeneity in Hurdle Models
185
636 Finite Mixture Versus Hurdle Models
186
637 Correlated Hurdle Models
187
64 ZeroInflated Count Data Models
188
642 ZeroInflated Poisson Model
189
643 ZeroInflated Negative Binomial Model
191
65 Compound Count Data Models
192
651 MultiEpisode Models
193
653 Count Amount Model
196
654 Endogenous Underreporting
197
66 Quantile Regression for Count Data
199
Correlated Count Data
203
711 Multivariate Poisson Distribution
205
712 Multivariate Negative Binomial Model
210
713 Multivariate PoissonGamma Mixture Model
212
714 Multivariate PoissonLogNormal Model
213
715 Latent PoissonNormal Model
216
716 MomentBased Methods
217
717 Copula Functions
219
72 Panel Data Models
220
721 Fixed Effects Poisson Model
222
722 Momentbased Estimation of the Fixed Effects Model
225
723 Fixed Effects Negative Binomial Model
227
724 Random Effects Count Data Models
228
725 Dynamic Panel Count Data Models
230
73 TimeSeries Count Data Models
232
Bayesian Analysis of Count Data
241
81 Bayesian Analysis of the Poisson Model
242
82 A Poisson Model with Underreporting
245
83 Estimation of the Multivariate PoissonLogNormal Model by MCMC
247
84 Estimation of a Random Coefficients Model by MCMC
248
Applications
251
92 Crime
252
94 Health Economics
254
95 Demography
257
96 Marketing and Management
260
97 Labor Mobility
261
971 Economics Models of Labor Mobility
262
972 Previous Literature
263
973 Data and Descriptive Statistics
265
974 Regression Results
269
975 Model Performance
272
976 Marginal Probability Effects
274
977 Structural Inferences
278
Probability Generating Functions
281
GaussHermite Quadrature
285
Software
288
Tables
291
References
299
Authors Index
321
Subject Index
326
Copyright

Common terms and phrases

Popular passages

Page 318 - Winkelmann, R. and KF Zimmermann (1995), Recent Developments in Count Data Modeling: Theory and Applications, Journal of Economic Surveys 9,1- 24.
Page 302 - Medicine 20, 3667-3676. van den Broek, J. (1995). A score test for zero inflation in a Poisson distribution.
Page 299 - Labour participation of Arab women: Estimates of the fertility to labour supply link," Applied Economics 30: 931-941.
Page 299 - The demand for children in Arab countries: Evidence from panel and count data models," Journal of Population Economics 11(3): 435-452.

References to this book

All Book Search results »

Bibliographic information